import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import github3
import json
# Get an API key for GitHub and set it as GITHUB_TOKEN
# Here is the URL to guide you on how to generate your GITHUB_TOKEN
# https://help.github.com/articles/creating-an-access-token-for-command-line-use/
# create your GitHub personal access tokens from https://github.com/settings/tokens
GITHUB_TOKEN = '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' # 'ADD-YOUR-GitHub-Personal-Token-HERE'
gh = github3.login(token=GITHUB_TOKEN)
from tqdm import tqdm
import dateutil
import datetime
import time
def getRepoIssue(repo):
print(repo)
ORG = repo[0]
REPO = repo[1]
FILENAME_ISSUES = ORG + REPO+ 'issues.json'
inputFile = open('./{}'.format(FILENAME_ISSUES), 'w')
today = datetime.date.today()
for i in tqdm(range(36)):#36
last_month = today + dateutil.relativedelta.relativedelta(months = -1)
types = 'type:issue'
repo = 'repo:angular/angular'
ranges = 'created:'+str(last_month)+'..'+str(today)
search_query = types + ' ' + repo + ' ' + ranges
for issue in gh.search_issues(search_query):
label_name=[]
data={}
current_issue = issue.as_json()
current_issue = json.loads(current_issue)
data['issue_number']=current_issue["number"] # Get issue number
data['created_at']= current_issue["created_at"][0:10] # Get created date of issue
if current_issue["closed_at"] == None:
data['closed_at']= current_issue["closed_at"]
else:
data['closed_at']= current_issue["closed_at"][0:10] # Get closed date of issue
for label in current_issue["labels"]:
label_name.append(label["name"]) # Get label name of issue
data['labels']= label_name
data['State'] = current_issue["state"] # It gives state of issue like closed or open
data['Author'] = current_issue["user"]["login"] # Get Author of issue
out=json.dumps(data) # save this all information to a JSON file
inputFile.write(out+ '\n')
today = last_month
time.sleep(10)
inputFile.close()
print('Done')
repolist = [('angular' ,'angular'),('angular' ,'material'),('angular' ,'angular-cli'),('SebastianM','angular-google-maps'),('d3','d3')]
for repo in repolist:
getRepoIssue(repo)
import os
repos = [x for x in os.listdir('./issues') if x[0] != '.']
dfs = pd.DataFrame()
for repo in repos:
list_of_issues_dict_data = [json.loads(line) for line in open('./issues/{}'.format(repo))]
issues_df = pd.DataFrame(list_of_issues_dict_data)
issues_df['repo'] = repo.split('.')[0]
dfs = dfs.append(issues_df,ignore_index=True)
import warnings
warnings.filterwarnings('ignore')
dfs.groupby(['repo']).count()[['issue_number']].plot(figsize=(25,10))
<AxesSubplot:xlabel='repo'>
dfs['create_month'] = dfs['created_at'].map(lambda x: int(x.split('-')[1]))
dfs.groupby(['create_month','repo']).create_month.count().unstack().plot(kind='bar',stacked=False,figsize=(25,10))
<AxesSubplot:xlabel='create_month'>
dfs6= dfs[dfs['closed_at'].notna()]
from datetime import datetime
dfs6['closed_weekday'] = [datetime.strptime(x,'%Y-%m-%d').weekday() for x in dfs6['closed_at']]
dfs6.groupby(['closed_weekday','repo']).closed_weekday.count()\
.unstack().plot(kind='bar',stacked=False,figsize=(25,10))
<AxesSubplot:xlabel='closed_weekday'>
#Add your code for requirement 7 in this cell
dfs.groupby(['repo']).count()[['closed_at','created_at']].plot(kind='bar',stacked=True,figsize=(25,10))
<AxesSubplot:xlabel='repo'>
import prophet
from prophet import Prophet
def r1(reponame):
df = dfs[dfs['repo'] ==reponame]
df = df.groupby('created_at')['created_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf
pdf['ds_new'] = pd.to_datetime(pdf['ds']) - pd.to_timedelta(7, unit='d')
df_weekly_max = pdf.reset_index().groupby([pd.Grouper(key='ds_new', freq='W-MON')]).apply(lambda x: x.loc[x.y == x.y.max(),['ds_orig','y']])
df_final = pd.DataFrame(['ds','y'])
df_final = df_weekly_max[['ds_orig','y']]
df_final.columns = ['ds','y']
m = Prophet()
m.fit(df_final)
future = m.make_future_dataframe(periods=365) # Forcast for 1 year
forecast = m.predict(future)
fig1 = m.plot(forecast)
for i in range(5):
r1(dfs['repo'].unique()[i])
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
def r2(reponame):
df = dfs6[dfs6['repo'] ==reponame]
df = df.groupby('closed_at')['closed_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf['ds_new'] = pd.to_datetime(pdf['ds']) - pd.to_timedelta(7, unit='d')
df_weekly_max = pdf.reset_index().groupby([pd.Grouper(key='ds_new', freq='W-MON')]).apply(lambda x: x.loc[x.y == x.y.max(),['ds_orig','y']])
df_final = pd.DataFrame(['ds','y'])
df_final = df_weekly_max[['ds_orig','y']]
df_final.columns = ['ds','y']
m = Prophet()
m.fit(df_final)
future = m.make_future_dataframe(periods=365) # Forcast for 1 year
forecast = m.predict(future)
fig1 = m.plot(forecast)
for i in range(5):
print(dfs['repo'].unique()[i])
r2(dfs['repo'].unique()[i])
angular-cliissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
angular-google-mapsissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
materialissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
d3issues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
angularissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
def r3(reponame):
df = dfs6[dfs6['repo'] ==reponame]
df['closed_at'] = df['closed_at'].map(lambda x: '-'.join(x.split('-')[:-1]+['01']))
df = df.groupby('closed_at')['closed_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf['ds_new'] = pd.to_datetime(pdf['ds'])
# pdf['ds_new'] = pd.to_datetime(pdf['ds']) - pd.to_timedelta(1, unit='m')
df_weekly_max = pdf.reset_index().groupby([pd.Grouper(key='ds_new', freq='Y')]).apply(lambda x: x.loc[x.y == x.y.max(),['ds_orig','y']])
df_final = pd.DataFrame(['ds','y'])
df_final = df_weekly_max[['ds_orig','y']]
df_final.columns = ['ds','y']
# df_final['ds'] = df_final['ds'].map(lambda x: '-'.join(x.split('-')[:-1]))
m = Prophet()
m.fit(df_final)
future = m.make_future_dataframe(periods=12) # Forcast for 1 year
forecast = m.predict(future)
fig1 = m.plot(forecast)
for i in range(5):
print(dfs['repo'].unique()[i])
r3(dfs['repo'].unique()[i])
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
angular-cliissues
INFO:prophet:n_changepoints greater than number of observations. Using 2. INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:n_changepoints greater than number of observations. Using 3.
angular-google-mapsissues
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:n_changepoints greater than number of observations. Using 3.
materialissues
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:n_changepoints greater than number of observations. Using 7.
d3issues
INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this. INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this. INFO:prophet:n_changepoints greater than number of observations. Using 2.
angularissues
def r4(reponame):
df = dfs[dfs['repo'] ==reponame]
df = df.groupby('created_at')['created_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf['ds_new'] = pd.to_datetime(pdf['ds'])
df_final = pd.DataFrame(['ds','y'])
df_final = pdf[['ds_orig','y']]
df_final.columns = ['ds','y']
m = Prophet()
m.fit(df_final)
future = m.make_future_dataframe(periods=365) # Forcast for 1 year
forecast = m.predict(future)
fig1 = m.plot(forecast)
for i in range(5):
print(dfs['repo'].unique()[i])
r4(dfs['repo'].unique()[i])
angular-cliissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
angular-google-mapsissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
materialissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
d3issues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
angularissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
def r5(reponame):
df = dfs6[dfs6['repo'] ==reponame]
df = df.groupby('closed_at')['closed_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf['ds_new'] = pd.to_datetime(pdf['ds'])
df_final = pd.DataFrame(['ds','y'])
df_final = pdf[['ds_orig','y']]
df_final.columns = ['ds','y']
m = Prophet()
m.fit(df_final)
future = m.make_future_dataframe(periods=365) # Forcast for 1 year
forecast = m.predict(future)
fig1 = m.plot(forecast)
for i in range(5):
print(dfs['repo'].unique()[i])
r5(dfs['repo'].unique()[i])
angular-cliissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
angular-google-mapsissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
materialissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
d3issues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
angularissues
INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
from tensorflow import keras
def plot_and_train(tensor_Created):
xtrain = tensor_Created.iloc[0:int(len(tensor_Created)*0.8)]['time']\
.to_numpy().reshape(int(len(tensor_Created)*0.8),1,1)
xtrain.shape
ytrain = tensor_Created.iloc[0:int(len(tensor_Created)*0.8)]['value']\
.astype('float32')\
.to_numpy()
ytrain.shape
model = keras.Sequential()
model.add(keras.layers.LSTM(
units=128,input_shape=(1,1)
))
model.add(keras.layers.Dropout(0.2))
model.add(keras.layers.Dense(units=1))
model.compile(
loss= 'mean_squared_error',optimizer='adam')
model.fit(xtrain,ytrain,epochs=30)
xtest = tensor_Created.iloc[int(len(tensor_Created)*0.8):]['time']\
.to_numpy().reshape(len(tensor_Created) - int(len(tensor_Created)*0.8),1,1)
ytest = tensor_Created.iloc[int(len(tensor_Created)*0.8):]['value']\
.astype('float32')\
.to_numpy()
ypred = model.predict(xtest)
fig,axs = plt.subplots(1,1,figsize=(20,8))
axs.plot([x for x in range(0,len(ytrain))],ytrain,'g',label='history')
axs.plot([x for x in range(len(ytrain),len(ytrain)+len(ytest))]
,ytest,marker='.',label='true')
axs.plot([x for x in range(len(ytrain),len(ytrain)+len(ytest))]
,ypred,'r',label='prediction')
plt.show()
def k1(reponame):
df = dfs[dfs['repo'] ==reponame]
df = df.groupby('created_at')['created_at']
df_new = df.describe()
dfnew1 = pd.Series(df_new['top'], name='ds_original')
df_new = pd.concat([df_new, dfnew1], axis=1)
datafrm_pdf = pd.DataFrame(['ds','ds_original','y'])
datafrm_pdf = df_new[['top','ds_original','count']]
datafrm_pdf.columns = ['ds','ds_original','y']
datafrm_pdf['ds_new'] = pd.to_datetime(datafrm_pdf['ds']) - pd.to_timedelta(7, unit='d')
df_weekly_maximum = datafrm_pdf.reset_index().groupby([pd.Grouper(key='ds_new', freq='W-MON')]).apply(lambda x: x.loc[x.y == x.y.max(),['ds_original','y']])
df_created_output = pd.DataFrame(['ds','y'])
df_created_output = df_weekly_maximum[['ds_original','y']]
df_created_output.columns = ['ds','y']
tensor_Created = df_created_output
tensor_Created = tensor_Created[['ds','y']]
df = pd.DataFrame(tensor_Created)
tensor_Created.rename(columns={'ds':'timestamp'}, inplace=True)
tensor_Created.rename(columns={'y':'value'}, inplace=True)
# print(tensor_Created)
firstDay = min(pd.to_datetime(tensor_Created['timestamp']))
tensor_Created['time'] = [float(x.days) for x in \
[x - firstDay for x in pd.to_datetime(tensor_Created['timestamp'])]]
return tensor_Created
for i in range(5):
plot_and_train(k1(dfs['repo'].unique()[i]))
Epoch 1/30 6/6 [==============================] - 2s 6ms/step - loss: 40.3379 Epoch 2/30 6/6 [==============================] - 0s 5ms/step - loss: 36.8622 Epoch 3/30 6/6 [==============================] - 0s 5ms/step - loss: 32.5123 Epoch 4/30 6/6 [==============================] - 0s 5ms/step - loss: 29.2733 Epoch 5/30 6/6 [==============================] - 0s 5ms/step - loss: 26.8783 Epoch 6/30 6/6 [==============================] - 0s 5ms/step - loss: 24.8575 Epoch 7/30 6/6 [==============================] - 0s 4ms/step - loss: 22.2255 Epoch 8/30 6/6 [==============================] - 0s 5ms/step - loss: 18.5114 Epoch 9/30 6/6 [==============================] - 0s 4ms/step - loss: 16.2103 Epoch 10/30 6/6 [==============================] - 0s 4ms/step - loss: 14.6249 Epoch 11/30 6/6 [==============================] - 0s 4ms/step - loss: 13.6604 Epoch 12/30 6/6 [==============================] - 0s 4ms/step - loss: 12.9889 Epoch 13/30 6/6 [==============================] - 0s 5ms/step - loss: 12.4530 Epoch 14/30 6/6 [==============================] - 0s 4ms/step - loss: 12.3520 Epoch 15/30 6/6 [==============================] - 0s 5ms/step - loss: 11.9230 Epoch 16/30 6/6 [==============================] - 0s 5ms/step - loss: 11.8813 Epoch 17/30 6/6 [==============================] - 0s 4ms/step - loss: 11.5843 Epoch 18/30 6/6 [==============================] - 0s 4ms/step - loss: 11.2211 Epoch 19/30 6/6 [==============================] - 0s 4ms/step - loss: 12.2544 Epoch 20/30 6/6 [==============================] - 0s 5ms/step - loss: 11.5786 Epoch 21/30 6/6 [==============================] - 0s 5ms/step - loss: 11.8827 Epoch 22/30 6/6 [==============================] - 0s 5ms/step - loss: 11.4665 Epoch 23/30 6/6 [==============================] - 0s 4ms/step - loss: 11.6020 Epoch 24/30 6/6 [==============================] - 0s 4ms/step - loss: 11.2635 Epoch 25/30 6/6 [==============================] - 0s 4ms/step - loss: 11.3555 Epoch 26/30 6/6 [==============================] - 0s 4ms/step - loss: 11.4392 Epoch 27/30 6/6 [==============================] - 0s 4ms/step - loss: 11.0738 Epoch 28/30 6/6 [==============================] - 0s 4ms/step - loss: 11.3233 Epoch 29/30 6/6 [==============================] - 0s 5ms/step - loss: 11.3702 Epoch 30/30 6/6 [==============================] - 0s 4ms/step - loss: 10.5266
Epoch 1/30 10/10 [==============================] - 2s 5ms/step - loss: 1.3680 Epoch 2/30 10/10 [==============================] - 0s 4ms/step - loss: 0.9297 Epoch 3/30 10/10 [==============================] - 0s 4ms/step - loss: 0.9457 Epoch 4/30 10/10 [==============================] - 0s 5ms/step - loss: 0.8875 Epoch 5/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8884 Epoch 6/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8891 Epoch 7/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8769 Epoch 8/30 10/10 [==============================] - 0s 4ms/step - loss: 0.7930 Epoch 9/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8864 Epoch 10/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8200 Epoch 11/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8949 Epoch 12/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8797 Epoch 13/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8599 Epoch 14/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8554 Epoch 15/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8680 Epoch 16/30 10/10 [==============================] - 0s 4ms/step - loss: 0.9468 Epoch 17/30 10/10 [==============================] - 0s 4ms/step - loss: 0.9029 Epoch 18/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8533 Epoch 19/30 10/10 [==============================] - 0s 5ms/step - loss: 0.8241 Epoch 20/30 10/10 [==============================] - 0s 5ms/step - loss: 0.8704 Epoch 21/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8317 Epoch 22/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8622 Epoch 23/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8217 Epoch 24/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8370 Epoch 25/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8806 Epoch 26/30 10/10 [==============================] - 0s 5ms/step - loss: 0.8320 Epoch 27/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8421 Epoch 28/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8322 Epoch 29/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8076 Epoch 30/30 10/10 [==============================] - 0s 4ms/step - loss: 0.8190
Epoch 1/30 8/8 [==============================] - 2s 5ms/step - loss: 7.8798 Epoch 2/30 8/8 [==============================] - 0s 4ms/step - loss: 5.6051 Epoch 3/30 8/8 [==============================] - 0s 5ms/step - loss: 3.0876 Epoch 4/30 8/8 [==============================] - 0s 4ms/step - loss: 2.0588 Epoch 5/30 8/8 [==============================] - 0s 4ms/step - loss: 1.7368 Epoch 6/30 8/8 [==============================] - 0s 4ms/step - loss: 1.4632 Epoch 7/30 8/8 [==============================] - 0s 4ms/step - loss: 1.2746 Epoch 8/30 8/8 [==============================] - 0s 4ms/step - loss: 1.2280 Epoch 9/30 8/8 [==============================] - 0s 4ms/step - loss: 1.1318 Epoch 10/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0540 Epoch 11/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0594 Epoch 12/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0111 Epoch 13/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0623 Epoch 14/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0393 Epoch 15/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9838 Epoch 16/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0185 Epoch 17/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9947 Epoch 18/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0015 Epoch 19/30 8/8 [==============================] - 0s 6ms/step - loss: 1.0380 Epoch 20/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9632 Epoch 21/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9555 Epoch 22/30 8/8 [==============================] - 0s 5ms/step - loss: 1.0006 Epoch 23/30 8/8 [==============================] - 0s 5ms/step - loss: 1.0337 Epoch 24/30 8/8 [==============================] - 0s 5ms/step - loss: 1.0210 Epoch 25/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9964 Epoch 26/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0016 Epoch 27/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9292 Epoch 28/30 8/8 [==============================] - 0s 4ms/step - loss: 1.0106 Epoch 29/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9905 Epoch 30/30 8/8 [==============================] - 0s 4ms/step - loss: 0.9380
Epoch 1/30 11/11 [==============================] - 2s 5ms/step - loss: 4.4384 Epoch 2/30 11/11 [==============================] - 0s 4ms/step - loss: 1.6000 Epoch 3/30 11/11 [==============================] - 0s 4ms/step - loss: 1.1197 Epoch 4/30 11/11 [==============================] - 0s 4ms/step - loss: 1.0347 Epoch 5/30 11/11 [==============================] - 0s 4ms/step - loss: 1.0518 Epoch 6/30 11/11 [==============================] - 0s 4ms/step - loss: 1.0223 Epoch 7/30 11/11 [==============================] - 0s 4ms/step - loss: 1.0516 Epoch 8/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9996 Epoch 9/30 11/11 [==============================] - 0s 5ms/step - loss: 1.0120 Epoch 10/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9465 Epoch 11/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9694 Epoch 12/30 11/11 [==============================] - 0s 4ms/step - loss: 1.0329 Epoch 13/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9703 Epoch 14/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9915 Epoch 15/30 11/11 [==============================] - 0s 5ms/step - loss: 0.9819 Epoch 16/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9942 Epoch 17/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9397 Epoch 18/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9910 Epoch 19/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9665 Epoch 20/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9727 Epoch 21/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9912 Epoch 22/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9899 Epoch 23/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9562 Epoch 24/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9485 Epoch 25/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9397 Epoch 26/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9171 Epoch 27/30 11/11 [==============================] - 0s 4ms/step - loss: 1.0077 Epoch 28/30 11/11 [==============================] - 0s 5ms/step - loss: 0.9504 Epoch 29/30 11/11 [==============================] - 0s 4ms/step - loss: 0.9841 Epoch 30/30 11/11 [==============================] - 0s 4ms/step - loss: 0.8971
--------------------------------------------------------------------------- KeyboardInterrupt Traceback (most recent call last) /tmp/ipykernel_91013/1009027695.py in <module> 1 for i in range(5): ----> 2 plot_and_train(k1(dfs['repo'].unique()[i])) /tmp/ipykernel_91013/3357273101.py in k1(reponame) 2 df = dfs[dfs['repo'] ==reponame] 3 df = df.groupby('created_at')['created_at'] ----> 4 df_new = df.describe() 5 dfnew1 = pd.Series(df_new['top'], name='ds_original') 6 df_new = pd.concat([df_new, dfnew1], axis=1) ~/py37/lib/python3.7/site-packages/pandas/core/groupby/generic.py in describe(self, **kwargs) 675 @doc(Series.describe) 676 def describe(self, **kwargs): --> 677 result = self.apply(lambda x: x.describe(**kwargs)) 678 if self.axis == 1: 679 return result.T ~/py37/lib/python3.7/site-packages/pandas/core/groupby/generic.py in apply(self, func, *args, **kwargs) 221 ) 222 def apply(self, func, *args, **kwargs): --> 223 return super().apply(func, *args, **kwargs) 224 225 @doc(_agg_template, examples=_agg_examples_doc, klass="Series") ~/py37/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in apply(self, func, *args, **kwargs) 1251 with option_context("mode.chained_assignment", None): 1252 try: -> 1253 result = self._python_apply_general(f, self._selected_obj) 1254 except TypeError: 1255 # gh-20949 ~/py37/lib/python3.7/site-packages/pandas/core/groupby/groupby.py in _python_apply_general(self, f, data) 1285 data after applying f 1286 """ -> 1287 keys, values, mutated = self.grouper.apply(f, data, self.axis) 1288 1289 return self._wrap_applied_output( ~/py37/lib/python3.7/site-packages/pandas/core/groupby/ops.py in apply(self, f, data, axis) 818 # group might be modified 819 group_axes = group.axes --> 820 res = f(group) 821 if not _is_indexed_like(res, group_axes, axis): 822 mutated = True ~/py37/lib/python3.7/site-packages/pandas/core/groupby/generic.py in <lambda>(x) 675 @doc(Series.describe) 676 def describe(self, **kwargs): --> 677 result = self.apply(lambda x: x.describe(**kwargs)) 678 if self.axis == 1: 679 return result.T ~/py37/lib/python3.7/site-packages/pandas/core/generic.py in describe(self, percentiles, include, exclude, datetime_is_numeric) 10018 exclude=exclude, 10019 datetime_is_numeric=datetime_is_numeric, > 10020 percentiles=percentiles, 10021 ) 10022 ~/py37/lib/python3.7/site-packages/pandas/core/describe.py in describe_ndframe(obj, include, exclude, datetime_is_numeric, percentiles) 93 ) 94 ---> 95 result = describer.describe(percentiles=percentiles) 96 return cast(FrameOrSeries, result) 97 ~/py37/lib/python3.7/site-packages/pandas/core/describe.py in describe(self, percentiles) 133 self.datetime_is_numeric, 134 ) --> 135 return describe_func(self.obj, percentiles) 136 137 ~/py37/lib/python3.7/site-packages/pandas/core/describe.py in describe_categorical_1d(data, percentiles_ignored) 261 names = ["count", "unique", "top", "freq"] 262 objcounts = data.value_counts() --> 263 count_unique = len(objcounts[objcounts != 0]) 264 if count_unique > 0: 265 top, freq = objcounts.index[0], objcounts.iloc[0] KeyboardInterrupt:
def k2(reponame):
df = dfs6[dfs6['repo'] ==reponame]
df = df.groupby('closed_at')['closed_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf
pdf['ds_new'] = pd.to_datetime(pdf['ds']) - pd.to_timedelta(7, unit='d')
df_weekly_max = pdf.reset_index().groupby([pd.Grouper(key='ds_new', freq='W-MON')]).apply(lambda x: x.loc[x.y == x.y.max(),['ds_orig','y']])
# print(df_weekly_max)
df_final = pd.DataFrame(['ds','y'])
df_final = df_weekly_max[['ds_orig','y']]
df_final.columns = ['timestamp','value']
firstDay = min(pd.to_datetime(df_final['timestamp']))
df_final['time'] = [float(x.days) for x in \
[x - firstDay for x in pd.to_datetime(df_final['timestamp'])]]
return df_final
for i in range(5):
plot_and_train(k2(dfs['repo'].unique()[i]))
Epoch 1/30 6/6 [==============================] - 2s 5ms/step - loss: 74.1200 Epoch 2/30 6/6 [==============================] - 0s 4ms/step - loss: 67.0349 Epoch 3/30 6/6 [==============================] - 0s 4ms/step - loss: 60.9096 Epoch 4/30 6/6 [==============================] - 0s 4ms/step - loss: 51.6273 Epoch 5/30 6/6 [==============================] - 0s 4ms/step - loss: 42.5123 Epoch 6/30 6/6 [==============================] - 0s 5ms/step - loss: 36.0137 Epoch 7/30 6/6 [==============================] - 0s 4ms/step - loss: 28.2219 Epoch 8/30 6/6 [==============================] - 0s 4ms/step - loss: 22.9476 Epoch 9/30 6/6 [==============================] - 0s 4ms/step - loss: 18.4147 Epoch 10/30 6/6 [==============================] - 0s 4ms/step - loss: 15.5896 Epoch 11/30 6/6 [==============================] - 0s 4ms/step - loss: 13.4435 Epoch 12/30 6/6 [==============================] - 0s 5ms/step - loss: 12.3899 Epoch 13/30 6/6 [==============================] - 0s 5ms/step - loss: 13.0265 Epoch 14/30 6/6 [==============================] - 0s 5ms/step - loss: 12.3130 Epoch 15/30 6/6 [==============================] - 0s 5ms/step - loss: 12.7757 Epoch 16/30 6/6 [==============================] - 0s 5ms/step - loss: 12.6435 Epoch 17/30 6/6 [==============================] - 0s 5ms/step - loss: 12.4903 Epoch 18/30 6/6 [==============================] - 0s 5ms/step - loss: 12.3464 Epoch 19/30 6/6 [==============================] - 0s 4ms/step - loss: 12.3604 Epoch 20/30 6/6 [==============================] - 0s 4ms/step - loss: 12.4186 Epoch 21/30 6/6 [==============================] - 0s 4ms/step - loss: 12.1640 Epoch 22/30 6/6 [==============================] - 0s 4ms/step - loss: 11.9021 Epoch 23/30 6/6 [==============================] - 0s 4ms/step - loss: 11.9905 Epoch 24/30 6/6 [==============================] - 0s 5ms/step - loss: 11.9494 Epoch 25/30 6/6 [==============================] - 0s 5ms/step - loss: 11.8648 Epoch 26/30 6/6 [==============================] - 0s 4ms/step - loss: 11.8564 Epoch 27/30 6/6 [==============================] - 0s 4ms/step - loss: 11.7569 Epoch 28/30 6/6 [==============================] - 0s 5ms/step - loss: 11.9083 Epoch 29/30 6/6 [==============================] - 0s 5ms/step - loss: 11.8245 Epoch 30/30 6/6 [==============================] - 0s 5ms/step - loss: 11.9770
Epoch 1/30 7/7 [==============================] - 2s 5ms/step - loss: 107.4818 Epoch 2/30 7/7 [==============================] - 0s 4ms/step - loss: 104.2688 Epoch 3/30 7/7 [==============================] - 0s 4ms/step - loss: 100.1935 Epoch 4/30 7/7 [==============================] - 0s 4ms/step - loss: 96.6306 Epoch 5/30 7/7 [==============================] - 0s 4ms/step - loss: 95.4873 Epoch 6/30 7/7 [==============================] - 0s 4ms/step - loss: 94.7735 Epoch 7/30 7/7 [==============================] - 0s 5ms/step - loss: 96.1811 Epoch 8/30 7/7 [==============================] - 0s 4ms/step - loss: 95.2301 Epoch 9/30 7/7 [==============================] - 0s 5ms/step - loss: 95.7991 Epoch 10/30 7/7 [==============================] - 0s 4ms/step - loss: 95.3945 Epoch 11/30 7/7 [==============================] - 0s 5ms/step - loss: 95.0832 Epoch 12/30 7/7 [==============================] - 0s 4ms/step - loss: 94.9185 Epoch 13/30 7/7 [==============================] - 0s 4ms/step - loss: 95.5069 Epoch 14/30 7/7 [==============================] - 0s 5ms/step - loss: 94.7043 Epoch 15/30 7/7 [==============================] - 0s 5ms/step - loss: 94.6864 Epoch 16/30 7/7 [==============================] - 0s 4ms/step - loss: 95.8631 Epoch 17/30 7/7 [==============================] - 0s 4ms/step - loss: 95.8533 Epoch 18/30 7/7 [==============================] - 0s 5ms/step - loss: 95.7566 Epoch 19/30 7/7 [==============================] - 0s 4ms/step - loss: 94.8071 Epoch 20/30 7/7 [==============================] - 0s 4ms/step - loss: 95.5525 Epoch 21/30 7/7 [==============================] - 0s 4ms/step - loss: 94.9128 Epoch 22/30 7/7 [==============================] - 0s 5ms/step - loss: 94.7027 Epoch 23/30 7/7 [==============================] - 0s 4ms/step - loss: 95.4677 Epoch 24/30 7/7 [==============================] - 0s 4ms/step - loss: 95.9445 Epoch 25/30 7/7 [==============================] - 0s 4ms/step - loss: 95.3466 Epoch 26/30 7/7 [==============================] - 0s 4ms/step - loss: 95.4067 Epoch 27/30 7/7 [==============================] - 0s 4ms/step - loss: 94.9906 Epoch 28/30 7/7 [==============================] - 0s 4ms/step - loss: 96.1412 Epoch 29/30 7/7 [==============================] - 0s 4ms/step - loss: 95.2857 Epoch 30/30 7/7 [==============================] - 0s 4ms/step - loss: 94.7068
Epoch 1/30 6/6 [==============================] - 2s 5ms/step - loss: 13.7108 Epoch 2/30 6/6 [==============================] - 0s 4ms/step - loss: 12.3670 Epoch 3/30 6/6 [==============================] - 0s 5ms/step - loss: 10.9014 Epoch 4/30 6/6 [==============================] - 0s 5ms/step - loss: 9.7541 Epoch 5/30 6/6 [==============================] - 0s 5ms/step - loss: 9.1348 Epoch 6/30 6/6 [==============================] - 0s 4ms/step - loss: 8.9016 Epoch 7/30 6/6 [==============================] - 0s 4ms/step - loss: 8.8539 Epoch 8/30 6/6 [==============================] - 0s 5ms/step - loss: 8.5987 Epoch 9/30 6/6 [==============================] - 0s 4ms/step - loss: 8.4934 Epoch 10/30 6/6 [==============================] - 0s 5ms/step - loss: 8.2805 Epoch 11/30 6/6 [==============================] - 0s 4ms/step - loss: 8.6209 Epoch 12/30 6/6 [==============================] - 0s 4ms/step - loss: 8.4046 Epoch 13/30 6/6 [==============================] - 0s 4ms/step - loss: 8.3703 Epoch 14/30 6/6 [==============================] - 0s 4ms/step - loss: 8.4253 Epoch 15/30 6/6 [==============================] - 0s 4ms/step - loss: 8.4068 Epoch 16/30 6/6 [==============================] - 0s 4ms/step - loss: 8.6456 Epoch 17/30 6/6 [==============================] - 0s 4ms/step - loss: 8.5101 Epoch 18/30 6/6 [==============================] - 0s 4ms/step - loss: 8.6263 Epoch 19/30 6/6 [==============================] - 0s 4ms/step - loss: 8.4544 Epoch 20/30 6/6 [==============================] - 0s 4ms/step - loss: 8.3588 Epoch 21/30 6/6 [==============================] - 0s 4ms/step - loss: 8.0978 Epoch 22/30 6/6 [==============================] - 0s 4ms/step - loss: 8.6571 Epoch 23/30 6/6 [==============================] - 0s 4ms/step - loss: 8.5062 Epoch 24/30 6/6 [==============================] - 0s 4ms/step - loss: 8.5077 Epoch 25/30 6/6 [==============================] - 0s 4ms/step - loss: 8.4776 Epoch 26/30 6/6 [==============================] - 0s 4ms/step - loss: 8.6114 Epoch 27/30 6/6 [==============================] - 0s 4ms/step - loss: 8.6961 Epoch 28/30 6/6 [==============================] - 0s 4ms/step - loss: 8.6640 Epoch 29/30 6/6 [==============================] - 0s 4ms/step - loss: 8.1770 Epoch 30/30 6/6 [==============================] - 0s 4ms/step - loss: 8.2620
Epoch 1/30 9/9 [==============================] - 2s 4ms/step - loss: 17.5175 Epoch 2/30 9/9 [==============================] - 0s 4ms/step - loss: 16.8972 Epoch 3/30 9/9 [==============================] - 0s 4ms/step - loss: 16.6780 Epoch 4/30 9/9 [==============================] - 0s 4ms/step - loss: 16.3357 Epoch 5/30 9/9 [==============================] - 0s 4ms/step - loss: 16.3795 Epoch 6/30 9/9 [==============================] - 0s 4ms/step - loss: 16.7081 Epoch 7/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4403 Epoch 8/30 9/9 [==============================] - 0s 4ms/step - loss: 16.2461 Epoch 9/30 9/9 [==============================] - 0s 4ms/step - loss: 16.5170 Epoch 10/30 9/9 [==============================] - 0s 4ms/step - loss: 16.6691 Epoch 11/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4504 Epoch 12/30 9/9 [==============================] - 0s 4ms/step - loss: 16.5629 Epoch 13/30 9/9 [==============================] - 0s 4ms/step - loss: 16.7080 Epoch 14/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4483 Epoch 15/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4633 Epoch 16/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4959 Epoch 17/30 9/9 [==============================] - 0s 4ms/step - loss: 16.3682 Epoch 18/30 9/9 [==============================] - 0s 4ms/step - loss: 16.5014 Epoch 19/30 9/9 [==============================] - 0s 4ms/step - loss: 16.6836 Epoch 20/30 9/9 [==============================] - 0s 4ms/step - loss: 16.3210 Epoch 21/30 9/9 [==============================] - 0s 4ms/step - loss: 16.2999 Epoch 22/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4754 Epoch 23/30 9/9 [==============================] - 0s 4ms/step - loss: 16.2987 Epoch 24/30 9/9 [==============================] - 0s 4ms/step - loss: 16.5386 Epoch 25/30 9/9 [==============================] - 0s 4ms/step - loss: 16.3680 Epoch 26/30 9/9 [==============================] - 0s 4ms/step - loss: 16.6911 Epoch 27/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4638 Epoch 28/30 9/9 [==============================] - 0s 4ms/step - loss: 16.4680 Epoch 29/30 9/9 [==============================] - 0s 4ms/step - loss: 16.3432 Epoch 30/30 9/9 [==============================] - 0s 4ms/step - loss: 16.3844
Epoch 1/30 5/5 [==============================] - 2s 4ms/step - loss: 159.6958 Epoch 2/30 5/5 [==============================] - 0s 4ms/step - loss: 150.1990 Epoch 3/30 5/5 [==============================] - 0s 4ms/step - loss: 142.5815 Epoch 4/30 5/5 [==============================] - 0s 4ms/step - loss: 136.9518 Epoch 5/30 5/5 [==============================] - 0s 4ms/step - loss: 128.3571 Epoch 6/30 5/5 [==============================] - 0s 4ms/step - loss: 120.7656 Epoch 7/30 5/5 [==============================] - 0s 5ms/step - loss: 112.6742 Epoch 8/30 5/5 [==============================] - 0s 4ms/step - loss: 105.1713 Epoch 9/30 5/5 [==============================] - 0s 5ms/step - loss: 97.7303 Epoch 10/30 5/5 [==============================] - 0s 5ms/step - loss: 85.8433 Epoch 11/30 5/5 [==============================] - 0s 4ms/step - loss: 77.2330 Epoch 12/30 5/5 [==============================] - 0s 5ms/step - loss: 68.3689 Epoch 13/30 5/5 [==============================] - 0s 4ms/step - loss: 58.3605 Epoch 14/30 5/5 [==============================] - 0s 4ms/step - loss: 50.6090 Epoch 15/30 5/5 [==============================] - 0s 4ms/step - loss: 43.5510 Epoch 16/30 5/5 [==============================] - 0s 5ms/step - loss: 40.1509 Epoch 17/30 5/5 [==============================] - 0s 4ms/step - loss: 36.9133 Epoch 18/30 5/5 [==============================] - 0s 4ms/step - loss: 33.8098 Epoch 19/30 5/5 [==============================] - 0s 4ms/step - loss: 31.3798 Epoch 20/30 5/5 [==============================] - 0s 4ms/step - loss: 30.7913 Epoch 21/30 5/5 [==============================] - 0s 4ms/step - loss: 29.3891 Epoch 22/30 5/5 [==============================] - 0s 4ms/step - loss: 28.7196 Epoch 23/30 5/5 [==============================] - 0s 4ms/step - loss: 28.4255 Epoch 24/30 5/5 [==============================] - 0s 4ms/step - loss: 28.9692 Epoch 25/30 5/5 [==============================] - 0s 4ms/step - loss: 29.4308 Epoch 26/30 5/5 [==============================] - 0s 5ms/step - loss: 27.8648 Epoch 27/30 5/5 [==============================] - 0s 5ms/step - loss: 29.4379 Epoch 28/30 5/5 [==============================] - 0s 5ms/step - loss: 29.1608 Epoch 29/30 5/5 [==============================] - 0s 5ms/step - loss: 28.6897 Epoch 30/30 5/5 [==============================] - 0s 5ms/step - loss: 27.9169
def k3(reponame):
df = dfs6[dfs6['repo'] ==reponame]
df['closed_at'] = df['closed_at'].map(lambda x: '-'.join(x.split('-')[:-1]+['01']))
df = df.groupby('closed_at')['closed_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf['ds_new'] = pd.to_datetime(pdf['ds'])
# pdf['ds_new'] = pd.to_datetime(pdf['ds']) - pd.to_timedelta(1, unit='m')
df_weekly_max = pdf.reset_index().groupby([pd.Grouper(key='ds_new', freq='Y')]).apply(lambda x: x.loc[x.y == x.y.max(),['ds_orig','y']])
print(df_weekly_max)
df_final = pd.DataFrame(['ds','y'])
df_final = df_weekly_max[['ds_orig','y']]
df_final.columns = ['ds','y']
# df_final['ds'] = df_final['ds'].map(lambda x: '-'.join(x.split('-')[:-1]))
df_final.columns = ['timestamp','value']
firstDay = min(pd.to_datetime(df_final['timestamp']))
df_final['time'] = [float(x.days/30) for x in \
[x - firstDay for x in pd.to_datetime(df_final['timestamp'])]]
return df_final
for i in range(5):
plot_and_train(k3(dfs['repo'].unique()[i]))
ds_orig y ds_new 2018-12-31 1 2018-11-01 154 2019-12-31 8 2019-06-01 148 2020-12-31 19 2020-05-01 135 2021-12-31 31 2021-05-01 204 Epoch 1/30 1/1 [==============================] - 2s 2s/step - loss: 21186.3379 Epoch 2/30 1/1 [==============================] - 0s 8ms/step - loss: 21174.1211 Epoch 3/30 1/1 [==============================] - 0s 6ms/step - loss: 21181.0234 Epoch 4/30 1/1 [==============================] - 0s 6ms/step - loss: 21138.5117 Epoch 5/30 1/1 [==============================] - 0s 7ms/step - loss: 21147.5215 Epoch 6/30 1/1 [==============================] - 0s 6ms/step - loss: 21103.4023 Epoch 7/30 1/1 [==============================] - 0s 6ms/step - loss: 21107.5410 Epoch 8/30 1/1 [==============================] - 0s 6ms/step - loss: 21125.9004 Epoch 9/30 1/1 [==============================] - 0s 7ms/step - loss: 21088.3848 Epoch 10/30 1/1 [==============================] - 0s 8ms/step - loss: 21069.5527 Epoch 11/30 1/1 [==============================] - 0s 6ms/step - loss: 21081.3926 Epoch 12/30 1/1 [==============================] - 0s 6ms/step - loss: 21035.0625 Epoch 13/30 1/1 [==============================] - 0s 5ms/step - loss: 21049.3613 Epoch 14/30 1/1 [==============================] - 0s 5ms/step - loss: 21029.3027 Epoch 15/30 1/1 [==============================] - 0s 5ms/step - loss: 20998.2598 Epoch 16/30 1/1 [==============================] - 0s 7ms/step - loss: 21021.0469 Epoch 17/30 1/1 [==============================] - 0s 5ms/step - loss: 21010.8301 Epoch 18/30 1/1 [==============================] - 0s 5ms/step - loss: 20995.4355 Epoch 19/30 1/1 [==============================] - 0s 6ms/step - loss: 20974.4629 Epoch 20/30 1/1 [==============================] - 0s 5ms/step - loss: 20975.2051 Epoch 21/30 1/1 [==============================] - 0s 5ms/step - loss: 20954.5176 Epoch 22/30 1/1 [==============================] - 0s 5ms/step - loss: 20931.6250 Epoch 23/30 1/1 [==============================] - 0s 5ms/step - loss: 20930.9453 Epoch 24/30 1/1 [==============================] - 0s 6ms/step - loss: 20861.2559 Epoch 25/30 1/1 [==============================] - 0s 6ms/step - loss: 20874.9043 Epoch 26/30 1/1 [==============================] - 0s 6ms/step - loss: 20902.3516 Epoch 27/30 1/1 [==============================] - 0s 8ms/step - loss: 20857.5879 Epoch 28/30 1/1 [==============================] - 0s 6ms/step - loss: 20857.0449 Epoch 29/30 1/1 [==============================] - 0s 7ms/step - loss: 20857.7520 Epoch 30/30 1/1 [==============================] - 0s 8ms/step - loss: 20815.6484
ds_orig y ds_new 2016-12-31 3 2016-09-01 21 2017-12-31 15 2017-09-01 28 2018-12-31 29 2018-11-01 219 2019-12-31 31 2019-01-01 37 2020-12-31 48 2020-07-01 34 2021-12-31 56 2021-06-01 27 Epoch 1/30 1/1 [==============================] - 3s 3s/step - loss: 12688.4199 Epoch 2/30 1/1 [==============================] - 0s 6ms/step - loss: 12659.1436 Epoch 3/30 1/1 [==============================] - 0s 5ms/step - loss: 12632.9893 Epoch 4/30 1/1 [==============================] - 0s 6ms/step - loss: 12621.5967 Epoch 5/30 1/1 [==============================] - 0s 5ms/step - loss: 12657.7139 Epoch 6/30 1/1 [==============================] - 0s 5ms/step - loss: 12619.1982 Epoch 7/30 1/1 [==============================] - 0s 7ms/step - loss: 12576.9375 Epoch 8/30 1/1 [==============================] - 0s 5ms/step - loss: 12568.8975 Epoch 9/30 1/1 [==============================] - 0s 5ms/step - loss: 12542.2715 Epoch 10/30 1/1 [==============================] - 0s 5ms/step - loss: 12543.8242 Epoch 11/30 1/1 [==============================] - 0s 5ms/step - loss: 12528.3682 Epoch 12/30 1/1 [==============================] - 0s 6ms/step - loss: 12524.0352 Epoch 13/30 1/1 [==============================] - 0s 5ms/step - loss: 12486.1396 Epoch 14/30 1/1 [==============================] - 0s 5ms/step - loss: 12475.4902 Epoch 15/30 1/1 [==============================] - 0s 6ms/step - loss: 12501.8203 Epoch 16/30 1/1 [==============================] - 0s 6ms/step - loss: 12464.9521 Epoch 17/30 1/1 [==============================] - 0s 5ms/step - loss: 12494.0283 Epoch 18/30 1/1 [==============================] - 0s 6ms/step - loss: 12452.4600 Epoch 19/30 1/1 [==============================] - 0s 5ms/step - loss: 12416.0205 Epoch 20/30 1/1 [==============================] - 0s 5ms/step - loss: 12418.7666 Epoch 21/30 1/1 [==============================] - 0s 6ms/step - loss: 12430.4590 Epoch 22/30 1/1 [==============================] - 0s 6ms/step - loss: 12463.5918 Epoch 23/30 1/1 [==============================] - 0s 6ms/step - loss: 12358.2354 Epoch 24/30 1/1 [==============================] - 0s 5ms/step - loss: 12355.5957 Epoch 25/30 1/1 [==============================] - 0s 5ms/step - loss: 12363.9561 Epoch 26/30 1/1 [==============================] - 0s 6ms/step - loss: 12324.6846 Epoch 27/30 1/1 [==============================] - 0s 5ms/step - loss: 12357.5088 Epoch 28/30 1/1 [==============================] - 0s 6ms/step - loss: 12324.7207 Epoch 29/30 1/1 [==============================] - 0s 5ms/step - loss: 12320.8730 Epoch 30/30 1/1 [==============================] - 0s 5ms/step - loss: 12324.1348
ds_orig y ds_new 2017-12-31 4 2017-06-01 40 2018-12-31 13 2018-03-01 43 2019-12-31 24 2019-02-01 46 2020-12-31 44 2020-11-01 44 2021-12-31 50 2021-06-01 10 Epoch 1/30 1/1 [==============================] - 2s 2s/step - loss: 1886.1960 Epoch 2/30 1/1 [==============================] - 0s 7ms/step - loss: 1882.0845 Epoch 3/30 1/1 [==============================] - 0s 7ms/step - loss: 1880.9705 Epoch 4/30 1/1 [==============================] - 0s 6ms/step - loss: 1870.4672 Epoch 5/30 1/1 [==============================] - 0s 5ms/step - loss: 1863.0278 Epoch 6/30 1/1 [==============================] - 0s 6ms/step - loss: 1857.0588 Epoch 7/30 1/1 [==============================] - 0s 5ms/step - loss: 1857.8097 Epoch 8/30 1/1 [==============================] - 0s 5ms/step - loss: 1847.3203 Epoch 9/30 1/1 [==============================] - 0s 5ms/step - loss: 1833.3953 Epoch 10/30 1/1 [==============================] - 0s 9ms/step - loss: 1845.6047 Epoch 11/30 1/1 [==============================] - 0s 6ms/step - loss: 1826.2349 Epoch 12/30 1/1 [==============================] - 0s 6ms/step - loss: 1838.9135 Epoch 13/30 1/1 [==============================] - 0s 6ms/step - loss: 1828.5477 Epoch 14/30 1/1 [==============================] - 0s 6ms/step - loss: 1824.9712 Epoch 15/30 1/1 [==============================] - 0s 7ms/step - loss: 1817.4939 Epoch 16/30 1/1 [==============================] - 0s 5ms/step - loss: 1801.7969 Epoch 17/30 1/1 [==============================] - 0s 5ms/step - loss: 1800.8901 Epoch 18/30 1/1 [==============================] - 0s 6ms/step - loss: 1785.0795 Epoch 19/30 1/1 [==============================] - 0s 7ms/step - loss: 1779.9589 Epoch 20/30 1/1 [==============================] - 0s 6ms/step - loss: 1797.8136 Epoch 21/30 1/1 [==============================] - 0s 7ms/step - loss: 1777.9838 Epoch 22/30 1/1 [==============================] - 0s 7ms/step - loss: 1769.2893 Epoch 23/30 1/1 [==============================] - 0s 6ms/step - loss: 1767.1208 Epoch 24/30 1/1 [==============================] - 0s 6ms/step - loss: 1765.4403 Epoch 25/30 1/1 [==============================] - 0s 6ms/step - loss: 1753.4735 Epoch 26/30 1/1 [==============================] - 0s 6ms/step - loss: 1748.7644 Epoch 27/30 1/1 [==============================] - 0s 6ms/step - loss: 1749.6602 Epoch 28/30 1/1 [==============================] - 0s 6ms/step - loss: 1740.8008 Epoch 29/30 1/1 [==============================] - 0s 6ms/step - loss: 1728.5815 Epoch 30/30 1/1 [==============================] - 0s 7ms/step - loss: 1716.7257
ds_orig y
ds_new
2014-12-31 4 2014-10-01 29
6 2014-12-01 29
2015-12-31 16 2015-10-01 76
2016-12-31 21 2016-03-01 52
2017-12-31 31 2017-01-01 42
2018-12-31 45 2018-03-01 15
2019-12-31 47 2019-08-01 12
2020-12-31 56 2020-05-01 7
2021-12-31 64 2021-01-01 7
69 2021-06-01 7
Epoch 1/30
1/1 [==============================] - 2s 2s/step - loss: 1530.9866
Epoch 2/30
1/1 [==============================] - 0s 6ms/step - loss: 1528.2532
Epoch 3/30
1/1 [==============================] - 0s 6ms/step - loss: 1532.1580
Epoch 4/30
1/1 [==============================] - 0s 6ms/step - loss: 1521.5898
Epoch 5/30
1/1 [==============================] - 0s 5ms/step - loss: 1518.0095
Epoch 6/30
1/1 [==============================] - 0s 6ms/step - loss: 1519.9308
Epoch 7/30
1/1 [==============================] - 0s 6ms/step - loss: 1513.2712
Epoch 8/30
1/1 [==============================] - 0s 6ms/step - loss: 1509.9646
Epoch 9/30
1/1 [==============================] - 0s 5ms/step - loss: 1501.1609
Epoch 10/30
1/1 [==============================] - 0s 5ms/step - loss: 1505.7505
Epoch 11/30
1/1 [==============================] - 0s 5ms/step - loss: 1496.9011
Epoch 12/30
1/1 [==============================] - 0s 4ms/step - loss: 1486.4440
Epoch 13/30
1/1 [==============================] - 0s 4ms/step - loss: 1487.8616
Epoch 14/30
1/1 [==============================] - 0s 4ms/step - loss: 1482.9243
Epoch 15/30
1/1 [==============================] - 0s 4ms/step - loss: 1481.1794
Epoch 16/30
1/1 [==============================] - 0s 6ms/step - loss: 1478.1934
Epoch 17/30
1/1 [==============================] - 0s 5ms/step - loss: 1475.4177
Epoch 18/30
1/1 [==============================] - 0s 5ms/step - loss: 1470.8655
Epoch 19/30
1/1 [==============================] - 0s 5ms/step - loss: 1464.6187
Epoch 20/30
1/1 [==============================] - 0s 5ms/step - loss: 1467.2003
Epoch 21/30
1/1 [==============================] - 0s 5ms/step - loss: 1458.7715
Epoch 22/30
1/1 [==============================] - 0s 5ms/step - loss: 1455.8928
Epoch 23/30
1/1 [==============================] - 0s 4ms/step - loss: 1449.6932
Epoch 24/30
1/1 [==============================] - 0s 5ms/step - loss: 1453.3782
Epoch 25/30
1/1 [==============================] - 0s 5ms/step - loss: 1439.6702
Epoch 26/30
1/1 [==============================] - 0s 4ms/step - loss: 1439.9167
Epoch 27/30
1/1 [==============================] - 0s 5ms/step - loss: 1432.2539
Epoch 28/30
1/1 [==============================] - 0s 4ms/step - loss: 1435.2278
Epoch 29/30
1/1 [==============================] - 0s 5ms/step - loss: 1433.1921
Epoch 30/30
1/1 [==============================] - 0s 5ms/step - loss: 1430.3213
ds_orig y ds_new 2018-12-31 1 2018-11-01 178 2019-12-31 13 2019-11-01 193 2020-12-31 16 2020-02-01 299 2021-12-31 31 2021-05-01 291 Epoch 1/30 1/1 [==============================] - 2s 2s/step - loss: 52750.5820 Epoch 2/30 1/1 [==============================] - 0s 7ms/step - loss: 52790.8711 Epoch 3/30 1/1 [==============================] - 0s 6ms/step - loss: 52735.7227 Epoch 4/30 1/1 [==============================] - 0s 6ms/step - loss: 52709.0469 Epoch 5/30 1/1 [==============================] - 0s 6ms/step - loss: 52682.9219 Epoch 6/30 1/1 [==============================] - 0s 6ms/step - loss: 52695.6992 Epoch 7/30 1/1 [==============================] - 0s 6ms/step - loss: 52662.8242 Epoch 8/30 1/1 [==============================] - 0s 6ms/step - loss: 52648.8320 Epoch 9/30 1/1 [==============================] - 0s 6ms/step - loss: 52629.8008 Epoch 10/30 1/1 [==============================] - 0s 7ms/step - loss: 52549.2617 Epoch 11/30 1/1 [==============================] - 0s 7ms/step - loss: 52590.3867 Epoch 12/30 1/1 [==============================] - 0s 6ms/step - loss: 52537.3906 Epoch 13/30 1/1 [==============================] - 0s 6ms/step - loss: 52545.3633 Epoch 14/30 1/1 [==============================] - 0s 6ms/step - loss: 52556.2500 Epoch 15/30 1/1 [==============================] - 0s 6ms/step - loss: 52439.8477 Epoch 16/30 1/1 [==============================] - 0s 6ms/step - loss: 52448.4375 Epoch 17/30 1/1 [==============================] - 0s 5ms/step - loss: 52479.0625 Epoch 18/30 1/1 [==============================] - 0s 6ms/step - loss: 52458.5625 Epoch 19/30 1/1 [==============================] - 0s 7ms/step - loss: 52431.2227 Epoch 20/30 1/1 [==============================] - 0s 7ms/step - loss: 52320.3477 Epoch 21/30 1/1 [==============================] - 0s 6ms/step - loss: 52363.5508 Epoch 22/30 1/1 [==============================] - 0s 5ms/step - loss: 52353.5430 Epoch 23/30 1/1 [==============================] - 0s 5ms/step - loss: 52267.8125 Epoch 24/30 1/1 [==============================] - 0s 5ms/step - loss: 52336.2852 Epoch 25/30 1/1 [==============================] - 0s 5ms/step - loss: 52317.0469 Epoch 26/30 1/1 [==============================] - 0s 5ms/step - loss: 52251.5000 Epoch 27/30 1/1 [==============================] - 0s 4ms/step - loss: 52248.7070 Epoch 28/30 1/1 [==============================] - 0s 5ms/step - loss: 52268.5508 Epoch 29/30 1/1 [==============================] - 0s 5ms/step - loss: 52212.7656 Epoch 30/30 1/1 [==============================] - 0s 6ms/step - loss: 52164.3086
def k4(reponame):
df = dfs[dfs['repo'] ==reponame]
df = df.groupby('created_at')['created_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf['ds_new'] = pd.to_datetime(pdf['ds'])
df_final = pd.DataFrame(['ds','y'])
df_final = pdf[['ds_orig','y']]
df_final.columns = ['timestamp','value']
firstDay = min(pd.to_datetime(df_final['timestamp']))
df_final['time'] = [float(x.days) for x in \
[x - firstDay for x in pd.to_datetime(df_final['timestamp'])]]
return df_final
for i in range(5):
plot_and_train(k4(dfs['repo'].unique()[i]))
Epoch 1/30 26/26 [==============================] - 2s 5ms/step - loss: 12.4158 Epoch 2/30 26/26 [==============================] - 0s 4ms/step - loss: 8.8909 Epoch 3/30 26/26 [==============================] - 0s 4ms/step - loss: 8.4112 Epoch 4/30 26/26 [==============================] - 0s 4ms/step - loss: 8.3992 Epoch 5/30 26/26 [==============================] - 0s 4ms/step - loss: 8.3049 Epoch 6/30 26/26 [==============================] - 0s 4ms/step - loss: 8.3289 Epoch 7/30 26/26 [==============================] - 0s 4ms/step - loss: 8.2537 Epoch 8/30 26/26 [==============================] - 0s 4ms/step - loss: 8.1962 Epoch 9/30 26/26 [==============================] - 0s 4ms/step - loss: 8.1856 Epoch 10/30 26/26 [==============================] - 0s 4ms/step - loss: 8.1767 Epoch 11/30 26/26 [==============================] - 0s 4ms/step - loss: 8.1561 Epoch 12/30 26/26 [==============================] - 0s 4ms/step - loss: 8.0360 Epoch 13/30 26/26 [==============================] - 0s 4ms/step - loss: 7.9757 Epoch 14/30 26/26 [==============================] - 0s 4ms/step - loss: 7.9456 Epoch 15/30 26/26 [==============================] - 0s 4ms/step - loss: 7.9509 Epoch 16/30 26/26 [==============================] - 0s 4ms/step - loss: 7.9493 Epoch 17/30 26/26 [==============================] - 0s 4ms/step - loss: 7.9753 Epoch 18/30 26/26 [==============================] - 0s 4ms/step - loss: 7.8558 Epoch 19/30 26/26 [==============================] - 0s 4ms/step - loss: 7.7772 Epoch 20/30 26/26 [==============================] - 0s 4ms/step - loss: 7.8784 Epoch 21/30 26/26 [==============================] - 0s 4ms/step - loss: 8.0094 Epoch 22/30 26/26 [==============================] - 0s 4ms/step - loss: 7.8180 Epoch 23/30 26/26 [==============================] - 0s 4ms/step - loss: 7.8393 Epoch 24/30 26/26 [==============================] - 0s 4ms/step - loss: 7.9391 Epoch 25/30 26/26 [==============================] - 0s 5ms/step - loss: 7.7241 Epoch 26/30 26/26 [==============================] - 0s 4ms/step - loss: 7.7861 Epoch 27/30 26/26 [==============================] - 0s 4ms/step - loss: 7.7549 Epoch 28/30 26/26 [==============================] - 0s 4ms/step - loss: 7.6759 Epoch 29/30 26/26 [==============================] - 0s 4ms/step - loss: 7.8657 Epoch 30/30 26/26 [==============================] - 0s 4ms/step - loss: 7.9070
Epoch 1/30 18/18 [==============================] - 2s 5ms/step - loss: 0.9510 Epoch 2/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6510 Epoch 3/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6786 Epoch 4/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6597 Epoch 5/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6554 Epoch 6/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6520 Epoch 7/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6168 Epoch 8/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6293 Epoch 9/30 18/18 [==============================] - 0s 5ms/step - loss: 0.6315 Epoch 10/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6391 Epoch 11/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6073 Epoch 12/30 18/18 [==============================] - 0s 5ms/step - loss: 0.6244 Epoch 13/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6086 Epoch 14/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6372 Epoch 15/30 18/18 [==============================] - 0s 5ms/step - loss: 0.6187 Epoch 16/30 18/18 [==============================] - 0s 5ms/step - loss: 0.6133 Epoch 17/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6369 Epoch 18/30 18/18 [==============================] - 0s 5ms/step - loss: 0.6186 Epoch 19/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6329 Epoch 20/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6049 Epoch 21/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6147 Epoch 22/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6169 Epoch 23/30 18/18 [==============================] - 0s 5ms/step - loss: 0.6086 Epoch 24/30 18/18 [==============================] - 0s 4ms/step - loss: 0.5950 Epoch 25/30 18/18 [==============================] - 0s 4ms/step - loss: 0.5994 Epoch 26/30 18/18 [==============================] - 0s 4ms/step - loss: 0.5905 Epoch 27/30 18/18 [==============================] - 0s 4ms/step - loss: 0.5917 Epoch 28/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6012 Epoch 29/30 18/18 [==============================] - 0s 4ms/step - loss: 0.5935 Epoch 30/30 18/18 [==============================] - 0s 4ms/step - loss: 0.6160
Epoch 1/30 15/15 [==============================] - 2s 5ms/step - loss: 4.1128 Epoch 2/30 15/15 [==============================] - 0s 4ms/step - loss: 1.7058 Epoch 3/30 15/15 [==============================] - 0s 4ms/step - loss: 1.3660 Epoch 4/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2856 Epoch 5/30 15/15 [==============================] - 0s 4ms/step - loss: 1.3332 Epoch 6/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2633 Epoch 7/30 15/15 [==============================] - 0s 5ms/step - loss: 1.3141 Epoch 8/30 15/15 [==============================] - 0s 4ms/step - loss: 1.3183 Epoch 9/30 15/15 [==============================] - 0s 5ms/step - loss: 1.3091 Epoch 10/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2423 Epoch 11/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2628 Epoch 12/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2983 Epoch 13/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2486 Epoch 14/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2171 Epoch 15/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2525 Epoch 16/30 15/15 [==============================] - 0s 5ms/step - loss: 1.3002 Epoch 17/30 15/15 [==============================] - 0s 5ms/step - loss: 1.2420 Epoch 18/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2880 Epoch 19/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2127 Epoch 20/30 15/15 [==============================] - 0s 5ms/step - loss: 1.2392 Epoch 21/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2492 Epoch 22/30 15/15 [==============================] - 0s 5ms/step - loss: 1.2321 Epoch 23/30 15/15 [==============================] - 0s 5ms/step - loss: 1.2221 Epoch 24/30 15/15 [==============================] - 0s 5ms/step - loss: 1.2793 Epoch 25/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2132 Epoch 26/30 15/15 [==============================] - 0s 5ms/step - loss: 1.2393 Epoch 27/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2196 Epoch 28/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2230 Epoch 29/30 15/15 [==============================] - 0s 4ms/step - loss: 1.2142 Epoch 30/30 15/15 [==============================] - 0s 4ms/step - loss: 1.1920
Epoch 1/30 18/18 [==============================] - 2s 5ms/step - loss: 4.1713 Epoch 2/30 18/18 [==============================] - 0s 5ms/step - loss: 1.2315 Epoch 3/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8706 Epoch 4/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8789 Epoch 5/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8460 Epoch 6/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8351 Epoch 7/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8347 Epoch 8/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8317 Epoch 9/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8502 Epoch 10/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8440 Epoch 11/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8504 Epoch 12/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8340 Epoch 13/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8609 Epoch 14/30 18/18 [==============================] - 0s 5ms/step - loss: 0.8159 Epoch 15/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8254 Epoch 16/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8131 Epoch 17/30 18/18 [==============================] - 0s 4ms/step - loss: 0.7905 Epoch 18/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8328 Epoch 19/30 18/18 [==============================] - 0s 5ms/step - loss: 0.8651 Epoch 20/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8556 Epoch 21/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8532 Epoch 22/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8065 Epoch 23/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8305 Epoch 24/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8091 Epoch 25/30 18/18 [==============================] - 0s 4ms/step - loss: 0.7912 Epoch 26/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8329 Epoch 27/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8195 Epoch 28/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8209 Epoch 29/30 18/18 [==============================] - 0s 4ms/step - loss: 0.7866 Epoch 30/30 18/18 [==============================] - 0s 4ms/step - loss: 0.8181
Epoch 1/30 28/28 [==============================] - 2s 5ms/step - loss: 50.6463 Epoch 2/30 28/28 [==============================] - 0s 4ms/step - loss: 25.5139 Epoch 3/30 28/28 [==============================] - 0s 4ms/step - loss: 19.1216 Epoch 4/30 28/28 [==============================] - 0s 4ms/step - loss: 18.2795 Epoch 5/30 28/28 [==============================] - 0s 4ms/step - loss: 18.4022 Epoch 6/30 28/28 [==============================] - 0s 5ms/step - loss: 18.1270 Epoch 7/30 28/28 [==============================] - 0s 4ms/step - loss: 18.4402 Epoch 8/30 28/28 [==============================] - 0s 4ms/step - loss: 18.2399 Epoch 9/30 28/28 [==============================] - 0s 4ms/step - loss: 17.7879 Epoch 10/30 28/28 [==============================] - 0s 5ms/step - loss: 17.8086 Epoch 11/30 28/28 [==============================] - 0s 4ms/step - loss: 17.5698 Epoch 12/30 28/28 [==============================] - 0s 4ms/step - loss: 17.8778 Epoch 13/30 28/28 [==============================] - 0s 4ms/step - loss: 17.6844 Epoch 14/30 28/28 [==============================] - 0s 5ms/step - loss: 17.5430 Epoch 15/30 28/28 [==============================] - 0s 4ms/step - loss: 17.4135 Epoch 16/30 28/28 [==============================] - 0s 4ms/step - loss: 17.6009 Epoch 17/30 28/28 [==============================] - 0s 4ms/step - loss: 17.4755 Epoch 18/30 28/28 [==============================] - 0s 4ms/step - loss: 17.3652 Epoch 19/30 28/28 [==============================] - 0s 4ms/step - loss: 17.5284 Epoch 20/30 28/28 [==============================] - 0s 4ms/step - loss: 17.3431 Epoch 21/30 28/28 [==============================] - 0s 4ms/step - loss: 17.2852 Epoch 22/30 28/28 [==============================] - 0s 4ms/step - loss: 17.3109 Epoch 23/30 28/28 [==============================] - 0s 4ms/step - loss: 17.1911 Epoch 24/30 28/28 [==============================] - 0s 4ms/step - loss: 17.1261 Epoch 25/30 28/28 [==============================] - 0s 4ms/step - loss: 17.0597 Epoch 26/30 28/28 [==============================] - 0s 4ms/step - loss: 17.3840 Epoch 27/30 28/28 [==============================] - 0s 4ms/step - loss: 16.9517 Epoch 28/30 28/28 [==============================] - 0s 4ms/step - loss: 17.2919 Epoch 29/30 28/28 [==============================] - 0s 4ms/step - loss: 17.0184 Epoch 30/30 28/28 [==============================] - 0s 4ms/step - loss: 17.2160
def k5(reponame):
df = dfs6[dfs6['repo'] ==reponame]
df = df.groupby('closed_at')['closed_at']
df_new = df.describe()
s1 = pd.Series(df_new['top'], name='ds_orig')
df_new = pd.concat([df_new, s1], axis=1)
pdf = pd.DataFrame(['ds','ds_orig','y'])
pdf = df_new[['top','ds_orig','count']]
pdf.columns = ['ds','ds_orig','y']
pdf['ds_new'] = pd.to_datetime(pdf['ds'])
df_final = pd.DataFrame(['ds','y'])
df_final = pdf[['ds_orig','y']]
df_final.columns = ['timestamp','value']
firstDay = min(pd.to_datetime(df_final['timestamp']))
df_final['time'] = [float(x.days) for x in \
[x - firstDay for x in pd.to_datetime(df_final['timestamp'])]]
return df_final
for i in range(5):
plot_and_train(k5(dfs['repo'].unique()[i]))
Epoch 1/30 24/24 [==============================] - 2s 4ms/step - loss: 17.2280 Epoch 2/30 24/24 [==============================] - 0s 4ms/step - loss: 10.0193 Epoch 3/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9911 Epoch 4/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9415 Epoch 5/30 24/24 [==============================] - 0s 4ms/step - loss: 9.1218 Epoch 6/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8811 Epoch 7/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9682 Epoch 8/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8696 Epoch 9/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9168 Epoch 10/30 24/24 [==============================] - 0s 4ms/step - loss: 9.0876 Epoch 11/30 24/24 [==============================] - 0s 4ms/step - loss: 8.7843 Epoch 12/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9226 Epoch 13/30 24/24 [==============================] - 0s 5ms/step - loss: 8.7900 Epoch 14/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9385 Epoch 15/30 24/24 [==============================] - 0s 5ms/step - loss: 8.9081 Epoch 16/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9308 Epoch 17/30 24/24 [==============================] - 0s 4ms/step - loss: 8.9300 Epoch 18/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8401 Epoch 19/30 24/24 [==============================] - 0s 4ms/step - loss: 8.7916 Epoch 20/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8162 Epoch 21/30 24/24 [==============================] - 0s 5ms/step - loss: 8.8432 Epoch 22/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8841 Epoch 23/30 24/24 [==============================] - 0s 5ms/step - loss: 8.8078 Epoch 24/30 24/24 [==============================] - 0s 5ms/step - loss: 8.6432 Epoch 25/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8110 Epoch 26/30 24/24 [==============================] - 0s 5ms/step - loss: 8.7156 Epoch 27/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8725 Epoch 28/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8172 Epoch 29/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8447 Epoch 30/30 24/24 [==============================] - 0s 4ms/step - loss: 8.8299
Epoch 1/30 11/11 [==============================] - 2s 5ms/step - loss: 71.9499 Epoch 2/30 11/11 [==============================] - 0s 5ms/step - loss: 69.3065 Epoch 3/30 11/11 [==============================] - 0s 5ms/step - loss: 66.4799 Epoch 4/30 11/11 [==============================] - 0s 4ms/step - loss: 66.4867 Epoch 5/30 11/11 [==============================] - 0s 4ms/step - loss: 66.6096 Epoch 6/30 11/11 [==============================] - 0s 4ms/step - loss: 66.3014 Epoch 7/30 11/11 [==============================] - 0s 4ms/step - loss: 66.6281 Epoch 8/30 11/11 [==============================] - 0s 4ms/step - loss: 66.2579 Epoch 9/30 11/11 [==============================] - 0s 4ms/step - loss: 65.8056 Epoch 10/30 11/11 [==============================] - 0s 4ms/step - loss: 66.0836 Epoch 11/30 11/11 [==============================] - 0s 4ms/step - loss: 66.1505 Epoch 12/30 11/11 [==============================] - 0s 4ms/step - loss: 66.1311 Epoch 13/30 11/11 [==============================] - 0s 4ms/step - loss: 66.4883 Epoch 14/30 11/11 [==============================] - 0s 4ms/step - loss: 65.9097 Epoch 15/30 11/11 [==============================] - 0s 4ms/step - loss: 65.8703 Epoch 16/30 11/11 [==============================] - 0s 4ms/step - loss: 65.8952 Epoch 17/30 11/11 [==============================] - 0s 4ms/step - loss: 66.2336 Epoch 18/30 11/11 [==============================] - 0s 4ms/step - loss: 66.1128 Epoch 19/30 11/11 [==============================] - 0s 4ms/step - loss: 65.9504 Epoch 20/30 11/11 [==============================] - 0s 5ms/step - loss: 66.2385 Epoch 21/30 11/11 [==============================] - 0s 4ms/step - loss: 66.2303 Epoch 22/30 11/11 [==============================] - 0s 5ms/step - loss: 66.2653 Epoch 23/30 11/11 [==============================] - 0s 4ms/step - loss: 65.9358 Epoch 24/30 11/11 [==============================] - 0s 5ms/step - loss: 66.0690 Epoch 25/30 11/11 [==============================] - 0s 4ms/step - loss: 65.8511 Epoch 26/30 11/11 [==============================] - 0s 5ms/step - loss: 65.9699 Epoch 27/30 11/11 [==============================] - 0s 4ms/step - loss: 66.4178 Epoch 28/30 11/11 [==============================] - 0s 4ms/step - loss: 66.2024 Epoch 29/30 11/11 [==============================] - 0s 4ms/step - loss: 66.1910 Epoch 30/30 11/11 [==============================] - 0s 4ms/step - loss: 66.0605
Epoch 1/30 10/10 [==============================] - 2s 5ms/step - loss: 8.9292 Epoch 2/30 10/10 [==============================] - 0s 4ms/step - loss: 6.9470 Epoch 3/30 10/10 [==============================] - 0s 4ms/step - loss: 5.8643 Epoch 4/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6360 Epoch 5/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6860 Epoch 6/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7387 Epoch 7/30 10/10 [==============================] - 0s 5ms/step - loss: 5.7698 Epoch 8/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7612 Epoch 9/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7114 Epoch 10/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6464 Epoch 11/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7188 Epoch 12/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7842 Epoch 13/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6384 Epoch 14/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6265 Epoch 15/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7127 Epoch 16/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6404 Epoch 17/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7008 Epoch 18/30 10/10 [==============================] - 0s 4ms/step - loss: 5.8206 Epoch 19/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6586 Epoch 20/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6355 Epoch 21/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7620 Epoch 22/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6820 Epoch 23/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7225 Epoch 24/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6736 Epoch 25/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6096 Epoch 26/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6549 Epoch 27/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6557 Epoch 28/30 10/10 [==============================] - 0s 4ms/step - loss: 5.6216 Epoch 29/30 10/10 [==============================] - 0s 4ms/step - loss: 5.7134 Epoch 30/30 10/10 [==============================] - 0s 4ms/step - loss: 5.5614
Epoch 1/30 14/14 [==============================] - 2s 5ms/step - loss: 12.3022 Epoch 2/30 14/14 [==============================] - 0s 5ms/step - loss: 10.7910 Epoch 3/30 14/14 [==============================] - 0s 5ms/step - loss: 10.6892 Epoch 4/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5773 Epoch 5/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5843 Epoch 6/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5515 Epoch 7/30 14/14 [==============================] - 0s 4ms/step - loss: 10.6324 Epoch 8/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5889 Epoch 9/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5843 Epoch 10/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5757 Epoch 11/30 14/14 [==============================] - 0s 5ms/step - loss: 10.4919 Epoch 12/30 14/14 [==============================] - 0s 4ms/step - loss: 10.3575 Epoch 13/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5638 Epoch 14/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5706 Epoch 15/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5260 Epoch 16/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5173 Epoch 17/30 14/14 [==============================] - 0s 4ms/step - loss: 10.3694 Epoch 18/30 14/14 [==============================] - 0s 4ms/step - loss: 10.4736 Epoch 19/30 14/14 [==============================] - 0s 4ms/step - loss: 10.3999 Epoch 20/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5394 Epoch 21/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5292 Epoch 22/30 14/14 [==============================] - 0s 4ms/step - loss: 10.4713 Epoch 23/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5423 Epoch 24/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5402 Epoch 25/30 14/14 [==============================] - 0s 5ms/step - loss: 10.5475 Epoch 26/30 14/14 [==============================] - 0s 4ms/step - loss: 10.4522 Epoch 27/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5995 Epoch 28/30 14/14 [==============================] - 0s 4ms/step - loss: 10.5846 Epoch 29/30 14/14 [==============================] - 0s 4ms/step - loss: 10.4304 Epoch 30/30 14/14 [==============================] - 0s 4ms/step - loss: 10.4471
Epoch 1/30 26/26 [==============================] - 2s 5ms/step - loss: 59.2835 Epoch 2/30 26/26 [==============================] - 0s 4ms/step - loss: 36.7836 Epoch 3/30 26/26 [==============================] - 0s 4ms/step - loss: 23.8981 Epoch 4/30 26/26 [==============================] - 0s 4ms/step - loss: 22.5529 Epoch 5/30 26/26 [==============================] - 0s 4ms/step - loss: 22.6094 Epoch 6/30 26/26 [==============================] - 0s 5ms/step - loss: 22.5058 Epoch 7/30 26/26 [==============================] - 0s 4ms/step - loss: 22.5513 Epoch 8/30 26/26 [==============================] - 0s 4ms/step - loss: 22.6628 Epoch 9/30 26/26 [==============================] - 0s 4ms/step - loss: 22.2868 Epoch 10/30 26/26 [==============================] - 0s 4ms/step - loss: 22.2767 Epoch 11/30 26/26 [==============================] - 0s 4ms/step - loss: 22.4944 Epoch 12/30 26/26 [==============================] - 0s 4ms/step - loss: 22.2507 Epoch 13/30 26/26 [==============================] - 0s 4ms/step - loss: 22.5445 Epoch 14/30 26/26 [==============================] - 0s 4ms/step - loss: 22.2991 Epoch 15/30 26/26 [==============================] - 0s 4ms/step - loss: 22.3727 Epoch 16/30 26/26 [==============================] - 0s 4ms/step - loss: 22.2287 Epoch 17/30 26/26 [==============================] - 0s 4ms/step - loss: 22.4616 Epoch 18/30 26/26 [==============================] - 0s 4ms/step - loss: 22.3937 Epoch 19/30 26/26 [==============================] - 0s 4ms/step - loss: 22.3577 Epoch 20/30 26/26 [==============================] - 0s 4ms/step - loss: 22.4429 Epoch 21/30 26/26 [==============================] - 0s 4ms/step - loss: 22.5724 Epoch 22/30 26/26 [==============================] - 0s 4ms/step - loss: 22.3399 Epoch 23/30 26/26 [==============================] - 0s 4ms/step - loss: 22.1661 Epoch 24/30 26/26 [==============================] - 0s 4ms/step - loss: 22.3228 Epoch 25/30 26/26 [==============================] - 0s 4ms/step - loss: 22.3715 Epoch 26/30 26/26 [==============================] - 0s 4ms/step - loss: 22.1556 Epoch 27/30 26/26 [==============================] - 0s 4ms/step - loss: 22.4790 Epoch 28/30 26/26 [==============================] - 0s 4ms/step - loss: 22.0194 Epoch 29/30 26/26 [==============================] - 0s 4ms/step - loss: 22.1329 Epoch 30/30 26/26 [==============================] - 0s 4ms/step - loss: 22.2931
#Add your code for requirement 8.6 in this cell
#Add your code for requirement 8.7 in this cell
#Add your code for requirement 8.8 in this cell
#Add your code for requirement 8.9 in this cell
#Add your code for requirement 8.10 in this cell
import statsmodels.api as sm
def plot_arima(df):
model = sm.tsa.ARIMA(np.asarray(df['value']),order=(1,0,0))
results = model.fit()
df['forecast'] = results.fittedvalues
df[['value','forecast']].plot(figsize=(16,12))
def s1(reponame):
df = dfs[dfs['repo'] ==reponame]
df = df.groupby('created_at')['created_at']
df_new = df.describe()
dfnew1 = pd.Series(df_new['top'], name='ds_original')
df_new = pd.concat([df_new, dfnew1], axis=1)
datafrm_pdf = pd.DataFrame(['ds','ds_original','y'])
datafrm_pdf = df_new[['top','ds_original','count']]
datafrm_pdf.columns = ['ds','ds_original','y']
datafrm_pdf['ds_new'] = pd.to_datetime(datafrm_pdf['ds']) - pd.to_timedelta(7, unit='d')
df_weekly_maximum = datafrm_pdf.reset_index().groupby([pd.Grouper(key='ds_new', freq='W-MON')]).apply(lambda x: x.loc[x.y == x.y.max(),['ds_original','y']])
df_created_output = pd.DataFrame(['ds','y'])
df_created_output = df_weekly_maximum[['ds_original','y']]
df_created_output.columns = ['ds','y']
tensor_Created = df_created_output
tensor_Created = tensor_Created[['ds','y']]
df = pd.DataFrame(tensor_Created)
tensor_Created.rename(columns={'ds':'timestamp'}, inplace=True)
tensor_Created.rename(columns={'y':'value'}, inplace=True)
# print(tensor_Created)
firstDay = min(pd.to_datetime(tensor_Created['timestamp']))
tensor_Created['time'] = [float(x.days) for x in \
[x - firstDay for x in pd.to_datetime(tensor_Created['timestamp'])]]
return tensor_Created
for i in range(5):
plot_arima(s1(dfs['repo'].unique()[i]))
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.00231D+00 |proj g|= 6.61249D-03
At iterate 5 f= 1.00230D+00 |proj g|= 4.44089D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 7 1 0 0 4.441D-08 1.002D+00
F = 1.0023012096178279
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.49041D+00 |proj g|= 3.77787D-04
At iterate 5 f= 1.49041D+00 |proj g|= 2.22045D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 7 1 0 0 2.220D-08 1.490D+00
F = 1.4904081681484020
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.21027D+00 |proj g|= 9.99978D-04
At iterate 5 f= 1.21027D+00 |proj g|= 8.88178D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 6 8 1 0 0 2.220D-08 1.210D+00
F = 1.2102729075654812
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.72903D+00 |proj g|= 3.14415D-04
At iterate 5 f= 2.72902D+00 |proj g|= 0.00000D+00
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 7 1 0 0 0.000D+00 2.729D+00
F = 2.7290231634489941
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.48925D+00 |proj g|= 3.55849D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 4 6 1 0 0 0.000D+00 2.489D+00
F = 2.4892494745459945
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.28527D+00 |proj g|= 9.82636D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 4 6 1 0 0 0.000D+00 2.285D+00
F = 2.2852653783830950
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.36152D+00 |proj g|= 4.69003D-04
At iterate 5 f= 2.36152D+00 |proj g|= 0.00000D+00
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 7 1 0 0 0.000D+00 2.362D+00
F = 2.3615240105152551
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.48765D+00 |proj g|= 6.35492D-05
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 4 6 1 0 0 0.000D+00 2.488D+00
F = 2.4876541128645582
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.97812D+00 |proj g|= 1.41442D-04
At iterate 5 f= 2.97812D+00 |proj g|= 4.44089D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 6 8 1 0 0 0.000D+00 2.978D+00
F = 2.9781200341588168
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.47522D+00 |proj g|= 2.19376D-03
At iterate 5 f= 2.47515D+00 |proj g|= 4.44089D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
for i in range(5):
plot_arima(k2(dfs['repo'].unique()[i]))
for i in range(5):
plot_arima(k3(dfs['repo'].unique()[i]))
ds_orig y
ds_new
2018-12-31 1 2018-11-01 154
2019-12-31 8 2019-06-01 148
2020-12-31 19 2020-05-01 135
2021-12-31 31 2021-05-01 204
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 6 12 1 0 0 0.000D+00 2.475D+00
F = 2.4751455753818767
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.19871D+00 |proj g|= 8.25140D-03
At iterate 5 f= 1.19862D+00 |proj g|= 8.88178D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 6 8 1 0 0 2.220D-08 1.199D+00
F = 1.1986245378645042
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.38866D+00 |proj g|= 6.55163D-03
At iterate 5 f= 1.38841D+00 |proj g|= 2.22045D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 11 1 0 0 2.220D-08 1.388D+00
F = 1.3884128384952434
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.24257D+00 |proj g|= 1.00990D-03
At iterate 5 f= 1.24257D+00 |proj g|= 0.00000D+00
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 7 1 0 0 0.000D+00 1.243D+00
F = 1.2425681160610045
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.58057D+00 |proj g|= 5.04325D-03
At iterate 5 f= 2.58027D+00 |proj g|= 7.28306D-06
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 7 10 1 0 0 4.441D-08 2.580D+00
F = 2.5802691455749467
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.51561D+00 |proj g|= 1.55693D-03
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 4 7 1 0 0 0.000D+00 2.516D+00
F = 2.5155687793120496
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 3.60167D+00 |proj g|= 3.60156D-05
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 3 5 1 0 0 0.000D+00 3.602D+00
F = 3.6016730858487187
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.51050D+00 |proj g|= 7.31504D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 4 6 1 0 0 0.000D+00 2.510D+00
F = 2.5104966502542587
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.71668D+00 |proj g|= 8.18456D-05
At iterate 5 f= 2.71668D+00 |proj g|= 0.00000D+00
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 8 1 0 0 0.000D+00 2.717D+00
F = 2.7166830815534944
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 3.24032D+00 |proj g|= 7.36078D-04
At iterate 5 f= 3.24032D+00 |proj g|= 6.35048D-06
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 7 9 1 0 0 0.000D+00 3.240D+00
F = 3.2403197250535545
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
ds_orig y
ds_new
2016-12-31 3 2016-09-01 21
2017-12-31 15 2017-09-01 28
2018-12-31 29 2018-11-01 219
2019-12-31 31 2019-01-01 37
2020-12-31 48 2020-07-01 34
2021-12-31 56 2021-06-01 27
ds_orig y
ds_new
2017-12-31 4 2017-06-01 40
2018-12-31 13 2018-03-01 43
2019-12-31 24 2019-02-01 46
2020-12-31 44 2020-11-01 44
2021-12-31 50 2021-06-01 10
ds_orig y
ds_new
2014-12-31 4 2014-10-01 29
6 2014-12-01 29
2015-12-31 16 2015-10-01 76
2016-12-31 21 2016-03-01 52
2017-12-31 31 2017-01-01 42
2018-12-31 45 2018-03-01 15
2019-12-31 47 2019-08-01 12
2020-12-31 56 2020-05-01 7
2021-12-31 64 2021-01-01 7
69 2021-06-01 7
ds_orig y
ds_new
2018-12-31 1 2018-11-01 178
2019-12-31 13 2019-11-01 193
2020-12-31 16 2020-02-01 299
2021-12-31 31 2021-05-01 291
for i in range(5):
plot_arima(k4(dfs['repo'].unique()[i]))
At iterate 0 f= 6.34853D+00 |proj g|= 1.17552D-01
At iterate 5 f= 4.58310D+00 |proj g|= 5.99076D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 8 16 1 0 0 0.000D+00 4.583D+00
F = 4.5830845630228509
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 5.66381D+00 |proj g|= 1.02003D-02
At iterate 5 f= 5.66327D+00 |proj g|= 9.73017D-03
At iterate 10 f= 5.65915D+00 |proj g|= 3.07665D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 13 16 1 0 0 0.000D+00 5.659D+00
F = 5.6591516192757583
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 5.24686D+00 |proj g|= 9.98161D-02
At iterate 5 f= 4.01263D+00 |proj g|= 4.80771D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 8 14 1 0 0 0.000D+00 4.013D+00
F = 4.0126291443313606
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 4.30622D+00 |proj g|= 2.98853D-02
At iterate 5 f= 4.30209D+00 |proj g|= 1.06113D-02
At iterate 10 f= 4.30139D+00 |proj g|= 4.44089D-07
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 11 14 1 0 0 0.000D+00 4.301D+00
F = 4.3013871465137417
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 5.58798D+00 |proj g|= 1.04811D-01
At iterate 5 f= 5.38783D+00 |proj g|= 5.12507D-02
At iterate 10 f= 5.37755D+00 |proj g|= 8.88178D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 13 23 1 0 0 0.000D+00 5.378D+00
F = 5.3775527819568829
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.40331D+00 |proj g|= 2.27240D-04
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 4 6 1 0 0 0.000D+00 2.403D+00
F = 2.4033083930313026
CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.11707D+00 |proj g|= 1.14739D-03
At iterate 5 f= 1.11707D+00 |proj g|= 4.44089D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 6 9 1 0 0 2.220D-08 1.117D+00
F = 1.1170656984462288
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.49041D+00 |proj g|= 3.77787D-04
At iterate 5 f= 1.49041D+00 |proj g|= 2.22045D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 5 7 1 0 0 2.220D-08 1.490D+00
F = 1.4904081681484020
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 1.21027D+00 |proj g|= 9.99978D-04
At iterate 5 f= 1.21027D+00 |proj g|= 8.88178D-08
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip = number of BFGS updates skipped
Nact = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F = final function value
* * *
N Tit Tnf Tnint Skip Nact Projg F
2 6 8 1 0 0 2.220D-08 1.210D+00
F = 1.2102729075654812
CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH
RUNNING THE L-BFGS-B CODE
* * *
Machine precision = 2.220D-16
N = 2 M = 12
At X0 0 variables are exactly at the bounds
At iterate 0 f= 2.72903D+00 |proj g|= 3.14415D-04
At iterate 5 f= 2.72902D+00 |proj g|= 0.00000D+00
* * *
Tit = total number of iterations
Tnf = total number of function evaluations
for i in range(5):
plot_arima(k5(dfs['repo'].unique()[i]))
#Add your code for requirement 8.6 in this cell
#Add your code for requirement 8.7 in this cell
#Add your code for requirement 8.8 in this cell
#Add your code for requirement 8.9 in this cell
#Add your code for requirement 8.10 in this cell